Human Activity Recognition With Accelerometer and Gyroscope: A Data Fusion Approach
| Autor: | Mitchell Webber, Raul Fernandez Rojas |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Data processing
business.industry Computer science Pattern recognition Gyroscope Kalman filter Sensor fusion Accelerometer computer.software_genre law.invention Activity recognition Feature (computer vision) law Artificial intelligence Electrical and Electronic Engineering business Instrumentation computer Data integration |
| Zdroj: | IEEE Sensors Journal. 21:16979-16989 |
| ISSN: | 2379-9153 1530-437X |
| Popis: | This paper compares the three levels of data fusion with the goal of determining the optimal level of data fusion for multi-sensor human activity data. Using the data processing pipeline, gyroscope and accelerometer data was fused at the sensor-level, feature-level and decision-level. For each level of data fusion four different techniques were used with varying levels of success. This analysis was performed on four human activity publicly-available datasets along with four well-known machine learning classifiers to validate the results. The decision-level fusion (Acc = 0.7443±0.0850) outperformed the other two levels of fusion in regards to accuracy, sensor level (Acc = 0.5934 ± 0.1110) and feature level (Acc = 0.6742 ± 0.0053), but, the processing time and computational power required for training and classification were far greater than practical for a HAR system. However, Kalman filter appear to be the more efficient method, since it exhibited both good accuracy (Acc = 0.7536 ± 0.1566) and short processing time (time = 61.71ms ± 63.85); properties that play a large role in real-time applications using wearable devices. The results of this study also serve as baseline information in the HAR literature to compare future methods of data fusion. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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